Correlation of significant places with self-reported state of bipolar disorder patients. - Matthia Sabatelli; Venet Osmani; Oscar Mayora; Agnes Grünerbl; Paul Lukowicz (MobiHealth, 2014)

Deep Transfer Learning for Art Classification Problems. - Matthia Sabatelli; Mike Kestemont; Walter Daelemans; Pierre Geurts (ECCV Workshops (2), 2018)

A Deep Convolutional Neural Network for Location Recognition and Geometry based Information. - Francesco Bidoia; Matthia Sabatelli; Amirhossein Shantia; Marco A. Wiering; Lambert Schomaker (ICPRAM, 2018)

Learning to Evaluate Chess Positions with Deep Neural Networks and Limited Lookahead. - Matthia Sabatelli; Francesco Bidoia; Valeriu Codreanu; Marco A. Wiering (ICPRAM, 2018)

Deep Quality-Value (DQV) Learning. - Matthia Sabatelli; Gilles Louppe; Pierre Geurts; Marco A. Wiering (BNAIC/BENELEARN, 2019)

Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithms. - Matthia Sabatelli; Gilles Louppe; Pierre Geurts; Marco A. Wiering (CoRR, 2019)

Forest Fire Control with Learning from Demonstration and Reinforcement Learning. - Travis Hammond; Dirk Jelle Schaap; Matthia Sabatelli; Marco A. Wiering (IJCNN, 2020)

The Deep Quality-Value Family of Deep Reinforcement Learning Algorithms. - Matthia Sabatelli; Gilles Louppe; Pierre Geurts; Marco A. Wiering (IJCNN, 2020)

QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement Learning. - Pascal Leroy; Damien Ernst; Pierre Geurts; Gilles Louppe; Jonathan Pisane; Matthia Sabatelli (CoRR, 2020)

Transfer Learning with Style Transfer between the Photorealistic and Artistic Domain. - Nikolay Banar; Matthia Sabatelli; Pierre Geurts; Walter Daelemans; Mike Kestemont (Computer Vision and Image Analysis of Art, 2021)

On the Transferability of Winning Tickets in Non-natural Image Datasets. - Matthia Sabatelli; Mike Kestemont; Pierre Geurts (VISIGRAPP (5: VISAPP), 2021)

Fractional Transfer Learning for Deep Model-Based Reinforcement Learning. - Remo Sasso; Matthia Sabatelli; Marco A. Wiering (CoRR, 2021)

On The Transferability of Deep-Q Networks. - Matthia Sabatelli; Pierre Geurts (CoRR, 2021)

Advances in Digital Music Iconography: Benchmarking the detection of musical instruments in unrestricted, non-photorealistic images from the artistic domain. - Matthia Sabatelli; Nikolay Banar; Marie Cocriamont; Eva Coudyzer; Karine Lasaracina; Walter Daelemans; Pierre Geurts; Mike Kestemont (Digit. Humanit. Q., 2021)

How Well Do Vision Transformers (VTs) Transfer to the Non-natural Image Domain? An Empirical Study Involving Art Classification. - Vincent Tonkes; Matthia Sabatelli (ECCV Workshops (1), 2022)

Safe and Psychologically Pleasant Traffic Signal Control with Reinforcement Learning using Action Masking. - Arthur Müller; Matthia Sabatelli (ITSC, 2022)

Machine Learning Students Overfit to Overfitting. - Matias Valdenegro-Toro; Matthia Sabatelli (Teaching ML, 2022)

Factors of Influence of the Overestimation Bias of Q-Learning. - Julius Wagenbach; Matthia Sabatelli (CoRR, 2022)

Contributions to Deep Transfer Learning: from Supervised to Reinforcement Learning. - Matthia Sabatelli (2022)

Bridging the Reality Gap of Reinforcement Learning based Traffic Signal Control using Domain Randomization and Meta Learning. - Arthur Müller; Matthia Sabatelli (ITSC, 2023)

Learning Perceptive Bipedal Locomotion over Irregular Terrain. - Bart van Marum; Matthia Sabatelli; Hamidreza Kasaei 001 (CoRR, 2023)

Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning. - Remo Sasso; Matthia Sabatelli; Marco A. Wiering (Trans. Mach. Learn. Res., 2023)

Smaller Batches, Bigger Gains? Investigating the Impact of Batch Sizes on Reinforcement Learning Based Real-World Production Scheduling. - Arthur Müller; Felix Grumbach; Matthia Sabatelli (ETFA, 2024)

Contextual Online Imitation Learning (COIL): Using Guide Policies in Reinforcement Learning. - Alexander Hill; Marc Groefsema; Matthia Sabatelli; Raffaella Carloni; Marco Grzegorczyk (ICAART (3), 2024)

Mapping Transformer Leveraged Embeddings for Cross-Lingual Document Representation. - Tsegaye Misikir Tashu; Eduard-Raul Kontos; Matthia Sabatelli; Matias Valdenegro-Toro (CoRR, 2024)

VDSC: Enhancing Exploration Timing with Value Discrepancy and State Counts. - Marius Captari; Remo Sasso; Matthia Sabatelli (CoRR, 2024)

ε-Optimally Solving Zero-Sum POSGs. - Erwan Escudie; Matthia Sabatelli; Jilles Dibangoye (CoRR, 2024)

Video-Driven Graph Network-Based Simulators. - Franciszek Szewczyk; Gilles Louppe; Matthia Sabatelli (CoRR, 2024)

Cross-Lingual Document Recommendations with Transformer-Based Representations: Evaluating Multilingual Models and Mapping Techniques. - Tsegaye Misikir Tashu; Eduard-Raul Kontos; Matthia Sabatelli; Matias Valdenegro-Toro (COLING Workshops, 2025)

Large-Image Object Detection for Fine-Grained Recognition of Punches Patterns in Medieval Panel Painting. - Josh Bruegger; Diana Ioana Catana; Vanja Macovaz; Matias Valdenegro-Toro; Matthia Sabatelli; Marco Zullich (EvoMUSART, 2025)

Upside-Down Reinforcement Learning for More Interpretable Optimal Control. - Juan Cardenas-Cartagena; Massimiliano Falzari; Marco Zullich; Matthia Sabatelli (ICAART (1), 2025)

Optimizing Initial Qubit Mappings Under Fixed Gate Error Rates Using Deep Reinforcement Learning. - Rares Adrian Oancea; Stan van der Linde; Willem de Kok; Matthia Sabatelli; Sebastian Feld (I4CS, 2025)

Interpretable Function Approximation with Gaussian Processes in Value-Based Model-Free Reinforcement Learning. - Matthijs van der Lende; Matthia Sabatelli; Juan Cardenas-Cartagena (NLDL, 2025)

Sparsity-Driven Plasticity in Multi-Task Reinforcement Learning. - Aleksandar Todorov; Juan Cardenas-Cartagena; Rafael Fernandes Cunha; Marco Zullich; Matthia Sabatelli (Trans. Mach. Learn. Res., 2025)

Fisher-Guided Selective Forgetting: Mitigating The Primacy Bias in Deep Reinforcement Learning. - Massimiliano Falzari; Matthia Sabatelli (CoRR, 2025)